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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    Larger sample sizes generally lead to increased precision when ... The resulting sample size formula, ... commute time for that sample. For example, person 1 takes 25 ...

  3. Lead time - Wikipedia

    en.wikipedia.org/wiki/Lead_time

    The lead time shows the amount of elapsed time from a chunk of work or story entering the backlog, to the end of the iteration or release. [13] A smaller lead time means that the process is more effective and the project team is more productive. [13] Lead time is also the saved time by starting an activity before its predecessor is completed.

  4. Sampling (statistics) - Wikipedia

    en.wikipedia.org/wiki/Sampling_(statistics)

    Formulas, tables, and power function charts are well known approaches to determine sample size. Steps for using sample size tables: Postulate the effect size of interest, α, and β. Check sample size table [20] Select the table corresponding to the selected α; Locate the row corresponding to the desired power; Locate the column corresponding ...

  5. Master production schedule - Wikipedia

    en.wikipedia.org/wiki/Master_production_schedule

    A master production schedule (MPS) is a plan for individual commodities to be produced in each time period such as production, staffing, inventory, etc. [1] It is usually linked to manufacturing where the plan indicates when and how much of each product will be demanded. [2]

  6. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    Given an r-sample statistic, one can create an n-sample statistic by something similar to bootstrapping (taking the average of the statistic over all subsamples of size r). This procedure is known to have certain good properties and the result is a U-statistic. The sample mean and sample variance are of this form, for r = 1 and r = 2.

  7. Nelson rules - Wikipedia

    en.wikipedia.org/wiki/Nelson_rules

    Nelson rules are a method in process control of determining whether some measured variable is out of control (unpredictable versus consistent). Rules for detecting "out-of-control" or non-random conditions were first postulated by Walter A. Shewhart [1] in the 1920s.